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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Leveraging explainable artificial intelligence to optimize clinical decision support.

Siru Liu1,2, Allison B McCoy1, Josh F Peterson1,3

  • 1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.

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Summary
This summary is machine-generated.

This study introduces a data-driven method using explainable artificial intelligence (XAI) to enhance clinical decision support (CDS) alert criteria. The approach successfully identified helpful suggestions for improving alerts and uncovered workflow issues.

Keywords:
clinical decision supportelectronic health recordexplainable artificial intelligence

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Area of Science:

  • Clinical Informatics
  • Artificial Intelligence in Healthcare
  • Machine Learning for Medical Decision Support

Background:

  • Clinical decision support (CDS) alerts are crucial for patient care but often require optimization.
  • Manual review of alert criteria can be time-consuming and may miss potential improvements.
  • Explainable Artificial Intelligence (XAI) offers novel methods for understanding and refining complex systems.

Purpose of the Study:

  • To develop and evaluate a data-driven process for generating actionable suggestions to improve CDS alert criteria.
  • To leverage explainable artificial intelligence (XAI) techniques for identifying optimal alert parameters.
  • To assess the utility of XAI-generated suggestions against historical data and expert clinical judgment.

Main Methods:

  • Extracted alert data from Vanderbilt University Medical Center (2019-2020).
  • Developed machine learning models to predict user responses to alerts.
  • Applied XAI techniques for global and local explanations of alert behavior.
  • Evaluated suggestions by comparing with alert change logs and conducting stakeholder interviews.

Main Results:

  • Utilized a dataset of 2,991,823 alert firings with 2689 features.
  • The LightGBM model achieved a high Area Under the ROC Curve of 0.919.
  • Identified 96 helpful suggestions for improving alert criteria.
  • Determined that 9.3% of alert firings could potentially be eliminated.
  • Uncovered workflow and education-related issues impacting alert acceptance.

Conclusions:

  • A data-driven process using XAI effectively generates suggestions for improving CDS alert criteria.
  • This approach can identify improvements that manual reviews might overlook or delay.
  • XAI serves a dual purpose: optimizing alert criteria and uncovering quality issues related to workflow, education, or staffing.